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--- |
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license: apache-2.0 |
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language: |
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- multilingual |
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- en |
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- ru |
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- es |
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- fr |
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- de |
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- it |
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- pt |
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- pl |
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- nl |
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- vi |
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- tr |
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- sv |
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- id |
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- ro |
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- cs |
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- zh |
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- hu |
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- ja |
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- th |
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- fi |
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- fa |
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- uk |
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- da |
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- el |
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- "no" |
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- bg |
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- sk |
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- ko |
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- ar |
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- lt |
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- ca |
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- sl |
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- he |
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- et |
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- lv |
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- hi |
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- sq |
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- ms |
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- az |
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- sr |
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- ta |
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- hr |
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- kk |
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- is |
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- ml |
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- mr |
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- te |
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- af |
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- gl |
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- fil |
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- be |
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- mk |
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- eu |
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- bn |
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- ka |
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- mn |
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- bs |
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- uz |
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- ur |
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- sw |
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- yue |
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- ne |
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- kn |
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- kaa |
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- gu |
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- si |
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- cy |
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- eo |
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- la |
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- hy |
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- ky |
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- tg |
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- ga |
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- mt |
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- my |
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- km |
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- tt |
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- so |
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- ku |
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- ps |
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- pa |
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- rw |
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- lo |
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- ha |
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- dv |
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- fy |
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- lb |
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- ckb |
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- mg |
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- gd |
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- am |
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- ug |
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- ht |
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- grc |
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- hmn |
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- sd |
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- jv |
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- mi |
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- tk |
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- ceb |
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- yi |
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- ba |
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- fo |
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- or |
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- xh |
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- su |
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- kl |
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- ny |
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- sm |
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- sn |
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- co |
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- zu |
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- ig |
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- yo |
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- pap |
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- st |
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- haw |
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- as |
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- oc |
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- cv |
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- lus |
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- tet |
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- gsw |
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- sah |
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- br |
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- rm |
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- sa |
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- bo |
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- om |
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- se |
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- ce |
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- cnh |
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- ilo |
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- hil |
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- udm |
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- os |
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- lg |
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- ti |
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- vec |
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- ts |
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- tyv |
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- kbd |
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- ee |
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- iba |
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- av |
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- kha |
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- to |
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- tn |
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- nso |
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- fj |
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- zza |
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- ak |
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- ada |
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- otq |
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- dz |
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- bua |
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- cfm |
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- ln |
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- chm |
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- gn |
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- krc |
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- wa |
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- hif |
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- yua |
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- srn |
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- war |
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- rom |
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- bik |
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- pam |
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- sg |
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- lu |
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- ady |
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- kbp |
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- syr |
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- ltg |
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- myv |
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- iso |
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- kac |
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- bho |
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- ay |
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- kum |
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- qu |
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- za |
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- pag |
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- ngu |
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- ve |
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- pck |
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- zap |
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- tyz |
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- hui |
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- bbc |
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- tzo |
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- tiv |
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- ksd |
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- gom |
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- min |
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- ang |
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- nhe |
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- bgp |
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- nzi |
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- nnb |
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- nv |
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- zxx |
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- bci |
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- kv |
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- new |
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- mps |
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- alt |
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- meu |
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- bew |
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- fon |
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- iu |
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- abt |
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- mgh |
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- mnw |
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- tvl |
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- dov |
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- tlh |
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- ho |
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- kw |
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- mrj |
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- meo |
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- crh |
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- mbt |
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- emp |
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- ace |
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- ium |
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- mam |
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- gym |
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- mai |
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- crs |
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- pon |
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- ubu |
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- fip |
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- quc |
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- gv |
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- kj |
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- btx |
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- ape |
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- chk |
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- rcf |
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- shn |
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- tzh |
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- mdf |
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- ppk |
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- ss |
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- gag |
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- cab |
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- kri |
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- seh |
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- ibb |
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- tbz |
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- bru |
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- enq |
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- ach |
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- cuk |
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- kmb |
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- wo |
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- kek |
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- qub |
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- tab |
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- bts |
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- kos |
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- rwo |
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- cak |
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- tuc |
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- bum |
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- cjk |
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- gil |
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- stq |
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- tsg |
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- quh |
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- mak |
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- arn |
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- ban |
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- jiv |
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- sja |
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- yap |
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- tcy |
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- toj |
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- twu |
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- xal |
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- amu |
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- rmc |
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- hus |
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- nia |
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- kjh |
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- bm |
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- guh |
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- mas |
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- acf |
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- dtp |
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- ksw |
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- bzj |
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- din |
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- zne |
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- mad |
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- msi |
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- mag |
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- mkn |
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- kg |
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- lhu |
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- ch |
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- qvi |
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- mh |
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- djk |
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- sus |
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- mfe |
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- srm |
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- dyu |
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- ctu |
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- gui |
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- pau |
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- inb |
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- bi |
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- mni |
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- guc |
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- jam |
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- wal |
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- jac |
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- bas |
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- gor |
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- skr |
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- nyu |
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- noa |
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- sda |
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- gub |
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- nog |
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- cni |
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- teo |
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- tdx |
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- sxn |
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- rki |
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- nr |
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- frp |
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- alz |
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- taj |
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- lrc |
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- cce |
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- rn |
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- jvn |
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- hvn |
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- nij |
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- dwr |
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- izz |
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- msm |
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- bus |
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- ktu |
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- chr |
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- maz |
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- tzj |
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- suz |
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- knj |
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- bim |
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- gvl |
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- bqc |
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- tca |
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- pis |
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- prk |
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- laj |
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- mel |
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- qxr |
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- niq |
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- ahk |
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- shp |
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- hne |
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- spp |
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- koi |
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- krj |
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- quf |
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- luz |
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- agr |
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- tsc |
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- mqy |
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- gof |
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- gbm |
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- miq |
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- dje |
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- awa |
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- bjj |
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- qvz |
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- sjp |
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- tll |
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- raj |
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- kjg |
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- bgz |
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- quy |
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- cbk |
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- akb |
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- oj |
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- ify |
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- mey |
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- ks |
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- cac |
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- brx |
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- qup |
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- syl |
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- jax |
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- ff |
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- ber |
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- tks |
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- trp |
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- mrw |
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- adh |
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- smt |
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- srr |
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- ffm |
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- qvc |
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- mtr |
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- ann |
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- kaa |
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- aa |
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- noe |
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- nut |
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- gyn |
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- kwi |
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- xmm |
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- msb |
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library_name: transformers |
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tags: |
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- text2text-generation |
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- text-generation-inference |
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datasets: |
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- allenai/MADLAD-400 |
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pipeline_tag: translation |
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widget: |
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- text: "<2en> Como vai, amigo?" |
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example_title: "Translation to English" |
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- text: "<2de> Do you speak German?" |
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example_title: "Translation to German" |
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|
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--- |
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|
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# Model Card for MADLAD-400-10B-MT |
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# Table of Contents |
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|
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0. [TL;DR](#TL;DR) |
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1. [Model Details](#model-details) |
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2. [Usage](#usage) |
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3. [Uses](#uses) |
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4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) |
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5. [Training Details](#training-details) |
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6. [Evaluation](#evaluation) |
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7. [Environmental Impact](#environmental-impact) |
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8. [Citation](#citation) |
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|
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# TL;DR |
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MADLAD-400-10B-MT is a multilingual machine translation model based on the T5 architecture that was |
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trained on 250 billion tokens covering over 450 languages using publicly available data. |
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It is competitive with models that are significantly larger. |
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|
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**Disclaimer**: [Juarez Bochi](https://huggingface.co./jbochi), who was not involved in this research, converted |
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the original weights and wrote the contents of this model card based on the original paper and Flan-T5. |
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|
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# Model Details |
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## Model Description |
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- **Model type:** Language model |
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- **Language(s) (NLP):** Multilingual (400+ languages) |
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- **License:** Apache 2.0 |
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- **Related Models:** [All MADLAD-400 Checkpoints](https://huggingface.co./models?search=madlad) |
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- **Original Checkpoints:** [All Original MADLAD-400 Checkpoints](https://github.com/google-research/google-research/tree/master/madlad_400) |
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- **Resources for more information:** |
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- [Research paper](https://arxiv.org/abs/2309.04662) |
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- [GitHub Repo](https://github.com/google-research/t5x) |
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- [Hugging Face MADLAD-400 Docs (Similar to T5) ](https://huggingface.co./docs/transformers/model_doc/MADLAD-400) - [Pending PR](https://github.com/huggingface/transformers/pull/27471) |
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# Usage |
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Find below some example scripts on how to use the model: |
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|
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## Using the Pytorch model with `transformers` |
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### Running the model on a CPU or GPU |
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<details> |
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<summary> Click to expand </summary> |
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First, install the Python packages that are required: |
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`pip install transformers accelerate sentencepiece` |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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model_name = 'google/madlad400-10b-mt' |
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model = T5ForConditionalGeneration.from_pretrained(model_name, device_map="auto") |
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tokenizer = T5Tokenizer.from_pretrained(model_name) |
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text = "<2pt> I love pizza!" |
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input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device) |
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outputs = model.generate(input_ids=input_ids) |
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tokenizer.decode(outputs[0], skip_special_tokens=True) |
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# Eu adoro pizza! |
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``` |
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</details> |
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## Running the model with Candle |
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<details> |
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<summary> Click to expand </summary> |
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Usage with [candle](https://github.com/huggingface/candle): |
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```bash |
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$ cargo run --example t5 --release -- \ |
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--model-id "google/madlad400-10b-mt" \ |
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--prompt "<2de> How are you, my friend?" \ |
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--decode --temperature 0 |
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``` |
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</details> |
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|
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# Uses |
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## Direct Use and Downstream Use |
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|
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> Primary intended uses: Machine Translation and multilingual NLP tasks on over 400 languages. |
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> Primary intended users: Research community. |
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## Out-of-Scope Use |
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> These models are trained on general domain data and are therefore not meant to |
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> work on domain-specific models out-of-the box. Moreover, these research models have not been assessed |
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> for production usecases. |
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# Bias, Risks, and Limitations |
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> We note that we evaluate on only 204 of the languages supported by these models and on machine translation |
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> and few-shot machine translation tasks. Users must consider use of this model carefully for their own |
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> usecase. |
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|
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## Ethical considerations and risks |
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|
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> We trained these models with MADLAD-400 and publicly available data to create baseline models that |
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> support NLP for over 400 languages, with a focus on languages underrepresented in large-scale corpora. |
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> Given that these models were trained with web-crawled datasets that may contain sensitive, offensive or |
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> otherwise low-quality content despite extensive preprocessing, it is still possible that these issues to the |
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> underlying training data may cause differences in model performance and toxic (or otherwise problematic) |
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> output for certain domains. Moreover, large models are dual use technologies that have specific risks |
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> associated with their use and development. We point the reader to surveys such as those written by |
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> Weidinger et al. or Bommasani et al. for a more detailed discussion of these risks, and to Liebling |
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> et al. for a thorough discussion of the risks of machine translation systems. |
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## Known Limitations |
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More information needed |
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## Sensitive Use: |
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More information needed |
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# Training Details |
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|
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> We train models of various sizes: a 3B, 32-layer parameter model, |
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> a 7.2B 48-layer parameter model and a 10.7B 32-layer parameter model. |
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> We share all parameters of the model across language pairs, |
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> and use a Sentence Piece Model with 256k tokens shared on both the encoder and decoder |
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> side. Each input sentence has a <2xx> token prepended to the source sentence to indicate the target |
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> language. |
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See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details. |
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|
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## Training Data |
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|
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> For both the machine translation and language model, MADLAD-400 is used. For the machine translation |
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> model, a combination of parallel datasources covering 157 languages is also used. Further details are |
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> described in the [paper](https://arxiv.org/pdf/2309.04662.pdf). |
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## Training Procedure |
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See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details. |
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# Evaluation |
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|
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## Testing Data, Factors & Metrics |
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> For evaluation, we used WMT, NTREX, Flores-200 and Gatones datasets as described in Section 4.3 in the [paper](https://arxiv.org/pdf/2309.04662.pdf). |
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|
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> The translation quality of this model varies based on language, as seen in the paper, and likely varies on |
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> domain, though we have not assessed this. |
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|
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## Results |
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 |
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 |
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 |
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See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details. |
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# Environmental Impact |
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More information needed |
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# Citation |
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**BibTeX:** |
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```bibtex |
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@misc{kudugunta2023madlad400, |
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title={MADLAD-400: A Multilingual And Document-Level Large Audited Dataset}, |
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author={Sneha Kudugunta and Isaac Caswell and Biao Zhang and Xavier Garcia and Christopher A. Choquette-Choo and Katherine Lee and Derrick Xin and Aditya Kusupati and Romi Stella and Ankur Bapna and Orhan Firat}, |
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year={2023}, |
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eprint={2309.04662}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
